ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation

Rinon Gal, Adi Haviv, Yuval Alaluf, Amit Haim Bermano, Daniel Cohen-Or, Gal Chechik

Keywords: Text-to-Image Generation, Workflow prediction, LLM Applications

TL;DR: We select text-to-image workflows to best match a user's text-to-image input prompt. Using these per-prompt flows leads to better image quality and prompt-alignment when compared to monolithic models or generic, prompt-idependent workflows.

Abstract: The practical use of text-to-image generation has evolved from simple, monolithic models to complex workflows that combine multiple specialized components. While workflow-based approaches can lead to improved image quality, crafting effective workflows requires significant expertise, owing to the large number of available components, their complex inter-dependence, and their dependence on the generation prompt. Here, we introduce the novel task of *prompt-adaptive workflow generation*, where the goal is to automatically tailor a workflow to each user prompt. We propose two LLM-based approaches to tackle this task: a tuning-based method that learns from user-preference data, and a training-free method that uses the LLM to select existing flows. Both approaches lead to improved image quality when compared to monolithic models or generic, prompt-independent workflows. Our work shows that prompt-dependent flow prediction offers a new pathway to improving text-to-image generation quality, complementing existing research directions in the field.

Primary Area: generative models

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Submission Number: 1608